Pediatric asthma has a substantial public health burden with numerous contributing risk factors in the physical, social, and health care environments. This burden is elevated among low-income minority children living in urban areas, related in part to multi-factorial residential exposures. There are numerous complex feedback loops among risk factors, with interventions potentially inducing behavioral responses that modify levels of other risk factors. Given these characteristics, pediatric asthma is an ideal candidate for systems science tools to prioritize among risk factors and design intervention strategies. However, there have been few applications of systems science techniques to asthma, and no applications to date that take account of multiple risk factors with housing, social and behavioral components. Discrete event simulation (DES), a systems science approach that involves modeling a complex system in which individuals can be tracked over time, multiple attributes can be incorporated simultaneously, and interactions and non-linear effects can be incorporated, is ideally suited for this application. This project (the Asthma Simulation Tool for Housing, Medication, and Social Adversity - ASTHMA) builds upon the only DES model of pediatric asthma or the physical environment, leveraging data from a longstanding electronic health record (EHR) database, and extensive neighborhood contextual data available from a BU center of environmental health disparities. Building on prior expertise and the strength of an established interdisciplinary team we propose the following specific aims: Aim 1: To build the Boston Children?s Healthcare Disparities Repository (BCHDR), a large database of predominantly low-income and racial/ethnic minority asthmatic children extracted from the BMC EHR, and use it to develop associations among key predictors of lung function, medication prescription and adherence, social adversity, asthma outcomes, and healthcare utilization. Aim 2: To predict exposure to indoor pollutants and environmental conditions across a range of housing types, resident behaviors, and sociodemographic characteristics, in a manner useful for DES modeling. Aim 3: To expand the existing DES model to capture the variability in housing, sociodemographic, and neighborhood environments for asthmatic children in the BCHDR, and evaluate the influence of implemented or proposed policies addressing housing conditions, medication adherence, and aspects of social adversity on asthma outcomes. These data and methods collectively allow us to develop unique insights about the individual and cumulative impacts of multiple stressors on asthma outcomes, as well as about how candidate interventions would influence asthma outcomes given the multiple pathways and complex behavioral responses. While we focus on data specific to Boston to ensure internal consistency, our methods and insights will generalize to other urban settings, our housing modeling approach can be expanded to any are in the US, and our discrete event simulation code and methodology for extracting and analyzing clinical and social risk factor data from an EHR could be adapted to other circumstances.